Look-Ahead Monte Carlo with People

Charles Blundell, UCL, London, United Kingdom

Adam Sanborn, University of Warwick

Tom Griffiths, University of California, Berkeley

Abstract

Investigating people's representations of categories of
complicated objects is a
difficult challenge, not least because of the large number of ways in which such
objects can vary. To make progress we need to take advantage of the structure of
object categories -- one compelling regularity is that object categories can be
described by a small number of dimensions. We present Look-Ahead Monte Carlo
with People, a method for exploring people's representations of a category where
there are many irrelevant dimensions.
This method combines ideas from Markov chain Monte Carlo with People, an
experimental paradigm derived from an algorithm for sampling complicated
distributions, with hybrid Monte Carlo, a technique that uses directional
information to construct efficient statistical sampling algorithms. We show that
even in a simple example, our approach takes advantage of the structure of object
categories to make experiments shorter and increase our ability to accurately
estimate category representations.